# HTUNEML - machine learning experiments monitoring and tuning
Quickstart: pip install htuneml. See the “Installing” section for more details.
Project links:
Examples
See the examples/ directory in the repository root for usage examples:
Requirements
To use all of the functionality of the library, you should have:
Python 2.6, 2.7, or 3.3+ (required)
PyAudio 0.2.11+ (required only if you need to use microphone input, Microphone)
Quick start
Register on website http://registru.ml, copy the api_key:
import htuneml as ht
job = Job('api_key')
@job.monitor
def train(par1=2,par2=2):
for i in range(par1):
#do training here
job.log({'loss':i*4,'ep':i})
job.setName('l2')
#job.debug()# uncomment and no experiment will be created and no logs sent
train(10, 2)
This will print out something like the following:
make experiment got key experimnet 5c5c8eaacbcfb9146641367a
Also it is possible to sent the parameters from the web app. First on gpu/cpu set the lisener:
import htuneml as ht
job = Job('api_key')
def train(par1=2,par2=2):
for i in range(par1):
#do training here
job.log({'loss':i*4,'ep':i})
job.sentParams(train)#sent the parameters list to the app
job.waitTask(train)#wait for parameters from app
Release files for htuneml 0.0.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| htuneml-0.0.6.tar.gz | 3.9 kB | Details |
Release files / htuneml-0.0.6.tar.gz
| Download URL | htuneml-0.0.6.tar.gz |
|---|---|
| Size | 3.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
26bda6a888a1f3c466ebb1c974fc6fb4cff41305aaa4dc7e9c0d35e8ae595c90
|
|
BLAKE2b-256 checksum How to use checksums |
26257ddf93bec63baf7bb27d76f55f7a94f0848360eba81a1a5eaba46378dec3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
Python-urllib/3.5
|